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"""Miscellaneous neural network functionality."""
from typing import Tuple, Type
from einops import rearrange
import torch
from torch import nn
def sliding_window(
images: torch.Tensor, patch_size: Tuple[int, int], stride: Tuple[int, int]
) -> torch.Tensor:
"""Creates patches of an image.
Args:
images (torch.Tensor): A Torch tensor of a 4D image(s), i.e. (batch, channel, height, width).
patch_size (Tuple[int, int]): The size of the patches to generate, e.g. 28x28 for EMNIST.
stride (Tuple[int, int]): The stride of the sliding window.
Returns:
torch.Tensor: A tensor with the shape (batch, patches, height, width).
"""
unfold = nn.Unfold(kernel_size=patch_size, stride=stride)
# Preform the slidning window, unsqueeze as the channel dimesion is lost.
patches = unfold(images).unsqueeze(1)
patches = rearrange(
patches, "b c (h w) t -> b t c h w", h=patch_size[0], w=patch_size[1]
)
return patches
def activation_function(activation: str) -> Type[nn.Module]:
"""Returns the callable activation function."""
activation_fns = nn.ModuleDict(
[
["gelu", nn.GELU()],
["leaky_relu", nn.LeakyReLU(negative_slope=1.0e-2, inplace=True)],
["none", nn.Identity()],
["relu", nn.ReLU(inplace=True)],
["selu", nn.SELU(inplace=True)],
]
)
return activation_fns[activation.lower()]
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